EP3958508A1 - Procédé et dispositif de traitement de données, support de stockage, et dispositif électronique - Google Patents

Procédé et dispositif de traitement de données, support de stockage, et dispositif électronique Download PDF

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Publication number
EP3958508A1
EP3958508A1 EP20791480.5A EP20791480A EP3958508A1 EP 3958508 A1 EP3958508 A1 EP 3958508A1 EP 20791480 A EP20791480 A EP 20791480A EP 3958508 A1 EP3958508 A1 EP 3958508A1
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Prior art keywords
data acquisition
data
application instance
network node
processing
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EP20791480.5A
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German (de)
English (en)
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EP3958508A4 (fr
Inventor
Wei Meng
Liya YUAN
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ZTE Corp
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ZTE Corp
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/12—Discovery or management of network topologies
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/16—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/06—Management of faults, events, alarms or notifications
    • H04L41/0631—Management of faults, events, alarms or notifications using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/08—Configuration management of networks or network elements
    • H04L41/0893—Assignment of logical groups to network elements
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/08—Configuration management of networks or network elements
    • H04L41/0896—Bandwidth or capacity management, i.e. automatically increasing or decreasing capacities
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/08—Configuration management of networks or network elements
    • H04L41/0896—Bandwidth or capacity management, i.e. automatically increasing or decreasing capacities
    • H04L41/0897—Bandwidth or capacity management, i.e. automatically increasing or decreasing capacities by horizontal or vertical scaling of resources, or by migrating entities, e.g. virtual resources or entities
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/147—Network analysis or design for predicting network behaviour
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/04—Arrangements for maintaining operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/16—Central resource management; Negotiation of resources or communication parameters, e.g. negotiating bandwidth or QoS [Quality of Service]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/08—Configuration management of networks or network elements
    • H04L41/0894—Policy-based network configuration management
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/34—Signalling channels for network management communication
    • H04L41/342—Signalling channels for network management communication between virtual entities, e.g. orchestrators, SDN or NFV entities

Definitions

  • the present application relates to the field of communications, and in particular, to a data processing method and device, a storage medium and an electronic device.
  • Network automation refers to the process of automatic configuration, management, testing, deployment, and operation of physical and virtual devices in a network.
  • the network under the support of the technology can automatically execute the established tasks and functions every day.
  • the network operations involving complex configuration and device management can be simplified through collaboration, automation, and network orchestration to accommodate business flexibility in a changing environment.
  • the network is increasingly complex, and the data generated by the network system also has the characteristics of diversity, multi-dimensionality, non-structuralization and the like. Since the data of the same business may have correlation, the traditional manual analysis processing mode has low efficiency and high cost, and the rule-based automatic processing mode has a narrow application range, is not flexible enough and has low accuracy. With the rise of big data and artificial intelligence, more and more intelligent analysis algorithms are also applied to the field of operation and maintenance. The system and the method play a great role in the aspects of problem positioning, flow prediction, decision assistance, intelligent alarm, automatic fault recovery and the like by analyzing mass data owned and generated by the operation and maintenance system, thereby further reducing the operation and maintenance cost.
  • the network in the related art does not have any concrete implementation schemes such as how to acquire network data and how to cooperate with an artificial intelligence management plane.
  • Embodiments of the disclosure provide a data processing method and device, a storage medium and an electronic device, which at least solve the problems that in the related technology, when big data and an artificial intelligence technology are introduced into network operation and maintenance management, network architecture cannot acquire network data and cooperate with an artificial intelligence management plane.
  • a data acquisition request method including: responsive to determining that an application instance is established, generating, by an orchestrator, a data acquisition request message corresponding to the application instance and sending the data acquisition request message to a next communication node, wherein the next communication node includes one of a network control device and a target network node; acquiring, by the target network node, data according to the data acquisition request message received from the orchestrator or through the network control device, and sending a data acquisition result to a processing device of the application instance; and processing, by the processing device of the application instance, the data acquisition result through a model in the application instance and outputting a processing result.
  • a data processing device including: a transmission module, provided in an orchestrator, and configured to generate a data acquisition request message corresponding to the application instance and send the data acquisition request message to a next communication node, wherein the next communication node includes one of a network control device and a target network node; an acquisition module, provided at the target network node, and configured to acquire data according to the data acquisition request message received from the orchestrator or via the network control device and send a data acquisition result to a processing device of the application instance; and a processing module, provided in the processing device of the application instance, and configured to process the data acquisition result through a model in the application instance and output a processing result.
  • a storage medium storing a computer program, wherein the computer program is configured to perform any one of the above method embodiments when executed.
  • an electronic device comprising a memory in which a computer program is stored and a processor configured to execute the computer program to perform any one of the above method embodiments.
  • the corresponding acquisition request is generated and sent to the network side. Therefore, the problem that network data cannot be acquired and the cooperation with the artificial intelligence management plane cannot be achieved through network architecture can be solved. Meanwhile, the automation, intellectualization and closed-loop control of the network can be effectively realized.
  • FIG. 1 is a flowchart of the request method of data acquisition according to the embodiment of the present disclosure. As shown in FIG. 1 , the flowchart includes steps of:
  • Types of orchestrators include, but are not limited to, a Machine Learning Function Orchestrator (MLFO).
  • MLFO Machine Learning Function Orchestrator
  • the orchestrator deploys application instances through an Artificial Intelligence (AI) platform.
  • Application instance management entries are pre-established in the orchestrator, and state machines in the entries are initialized.
  • information which is successfully established is sent to an orchestrator, such that a corresponding application instance management entry in the orchestrator is activated.
  • a state machine in the entry is set to be enabled, other information in the entry is assigned according to the condition of the instance, and a keep-alive message is sent to the processing device of the application instance at intervals to determine a use state of the instance.
  • the management entries include information such as a state machine, a position, a type, a model and algorithm combination, a life cycle, a data preprocessing algorithm and data acquisition requirements of each model of the application instance.
  • the network control device refers to a network orchestration or control plane at a network side, for example, a Network Slice Management Function (NSMF).
  • NSMF Network Slice Management Function
  • the type of target network node can also be adjusted according to the specific implementation.
  • the target network node may be a Network Subnet Slice Management Function (NSSMF) element in the 5th Generation (5G) communication system, and a 5G Network Slice.
  • NSSMF Network Subnet Slice Management Function
  • 5G 5th Generation
  • the target network node may also be an alarm collection platform.
  • the data acquisition request message includes: location information of the application instance, network node information and data acquisition content.
  • the method further includes: determining, by the network control device, the target network node for data acquisition according to the network node information in the data acquisition request message; and sending, by the network control device, the data acquisition request message to the target network node.
  • the network node information has information about one or more target network nodes.
  • the information about one or more target network nodes can be configured to instruct the network control device to send the data acquisition request message to multiple target network nodes at the same time, or can be configured to instruct the network control device to send the data acquisition request message to one or more of the multiple target network nodes, where the target network nodes serve as intermediate nodes to forward the data acquisition request message to other target network nodes.
  • the specific number thereof is determined by the orchestrator according to the number and type of application instances and the model included in the use instance.
  • the orchestrator can directly send the data acquisition request message to the target network node, and the network node information at this time can also be configured to assist a network node receiving the information to judge whether the node is the target network node of the orchestrator. If the network node information is correct, the network node will perform corresponding data acquisition and can also respond to the orchestrator. If the network node information is wrong, the network node will refuse to execute data acquisition and send error response information to the orchestrator so that the orchestrator finds a suitable target network node in time.
  • the method further includes: receiving, by the network control device, the processing result output from the processing device of the application instance; and configuring, by the network control device, the network according to the processing result.
  • the network control device Since the network control device is used for controlling the network side, after receiving the processing result output from the processing device, the network control device can optimize the network environment by means of a network configuration according to a decision of the application instance.
  • the step of acquiring, by the target network node, data according to the data acquisition request message received from the orchestrator or through the network control device includes: determining, by the target network node, an application instance needing data acquisition according to the location information of the application instance; and acquiring, by the target network node, data corresponding to the application instance.
  • the step of acquiring, by the target network node, data corresponding to the application instance includes: screening, by the target network node, the data acquisition content according to a data feature set; and acquiring, by the target network node, the data of the screened data acquisition content.
  • FIG. 2 is a schematic diagram illustrating a screening of a data option according to an embodiment of the disclosure, and as shown in FIG. 2 , a bitmap way can be employed to screen information to be collected in a data feature set.
  • the data feature set is determined by: determining, by an encoder, the data feature set according to a network requirement of the application instance and sending the data feature set to the next communication node, or after receiving the data acquisition request message, analyzing, by the target network node, the network requirement of the application instance corresponding to the location information of the application instance and determining the data feature set.
  • the data feature set includes: network information of a network in which the target network node is located; user information of the target network node; device information of a device in which the target network node is located; and data message information of the target network node.
  • the network node sides mutually have the data feature set, and the network element equipment can acquire data according to the data feature set.
  • the network information includes an Internet Protocol (IP) address, a Protocol number, and a port number.
  • IP Internet Protocol
  • the user information includes: user Identifier (ID), charging attributes, the number of books used.
  • the device information can be environmental parameters such as the fan rotation speed, the temperature and the humidity of the current device.
  • the data message information can include: type of message, flow, delay, for example.
  • the data acquisition content includes one of: the number and type of users, user session information, user traffic information, data acquisition period and network service alarm information.
  • the step of processing, by the processing device of the application instance, the data acquisition result through the model in the application instance includes: sorting, by the processing device, the data acquisition result according to a type of the data acquisition result and configuring the sorted data acquisition result into a corresponding model; and processing, by the processing device, the sorted data acquisition result in the model, wherein the processing manner includes predicting the data and inferring the data.
  • the model is a system which is obtained by learning from existing data or experience through methods such as machine learning and deep learning and realizes functions such as specific analysis and prediction.
  • Each model has its established functionality. For example, some models can be configured to predict an amount of time when new users and sessions reach a required number. As another example, some models can be configured to predict the amount of time to perform slice expansion. In addition, the position of the alarm in the equipment can be determined according to the quantity or the type of the alarm information. In the meanwhile, the correlation exists among the models. For example, predicting an amount of time when new users and sessions reach a required number is used as an input of predicting the amount of time to perform slice expansion. Therefore, the models can be connected in series. The function of the specific model is dependent on the function of the application instance.
  • the processing device can perform corresponding processing operation, such as filtering, screening, matching, or sorting.
  • the processed data is continuously predicted in other models in the application instance, and a final processing result is output after inference until the processing is completed.
  • Prediction refers to a time, position and numerical value corresponding to resource consumption when the data acquisition content reaches the preset result. For example, it is predicted new users and sessions reach a peak number in 2 hours.
  • Prediction refers to an amount of time or location when or where the data acquisition content can reach a predetermined result. For example, within 2 hours, the new users and sessions reach a peak number. For another example, a flow of traffic used by a certain user on weekends is predicted to be 10G according to the user's habit.
  • Inference is a conclusion inferred according to the data acquisition content or the predicted result. For example, if the number of new users and sessions reach the peak within 2 hours, it is inferred that the slice expansion needs to be performed after 1.5 hours.
  • the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation.
  • the technical solution of the present application can be embodied in the form of a software product, where the computer software product is stored in a storage medium (e.g., ROM/RAM, magnetic disk, or optical disk), and includes several instructions for enabling a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.,) to execute the method according to the embodiments of the disclosure.
  • a storage medium e.g., ROM/RAM, magnetic disk, or optical disk
  • Scenario 1 the scheme of 5G core network slice utilization rate prediction and intelligent deployment is realized by applying artificial intelligence.
  • FIG. 3 is a schematic diagram based on scenario 1 according to an embodiment of the present disclosure.
  • the Orchestrator is presented in the form of an MLFO (Machine Learning Function Orchestrator) in this scenario, enabling interaction with the application instance and the NSMF.
  • MLFO Machine Learning Function Orchestrator
  • the application instance is embedded in an NWDAF (Network Data Analysis Function) element in the form of micro-service, and includes a data preprocessing module, a model 1 and a model 2, wherein the model 1 predicts the flow of traffic and the number of users in the 5G core Network slice service in the future 2 hours, and the model 2 calculates the prediction result and analyzes whether capacity expansion or capacity reduction is required.
  • NWDAF Network Data Analysis Function
  • the data acquisition device is embedded in the network element of NSSMF and 5G network slice.
  • the application instance 1 (hereinafter referred to as instance 1) is successfully established in the NWDAF and notifies the MLFO; the MLFO activates the management items of the artificial intelligence instance 1, sets a state machine of the instance 1 as a running state, and sends a keep-alive message every 60 seconds to confirm the state of instance 1.
  • the data acquisition request message includes: IP address of instance 1 (location information of application instance), specifying data acquisition from NSSMF and 5G network slice 1 (target network element).
  • the acquired contents include the number of existing users, session information of user, session information newly established every second, traffic information of user, timestamp, and holiday factor, for example (data acquisition requirement), and the data acquisition cycle is 100ms.
  • the MLFO sends the data acquisition request message to the NSMF.
  • the NSMF analyzes the contents of the data acquisition request message and sends the IP address and data acquisition requirement of instance 1 to the designated NSSMF.
  • the NSSMF continues to send the IP address and data acquisition requirement of instance 1 to the designated 5G network slice 1.
  • the NSSMF and 5G network slice 1 acquire information including the number of users, user session information, session information newly established very second, user traffic information, timestamp and holiday factor, and send the above information to application instance 1.
  • a data preprocessing module sorts the data and sends the data to a model 1.
  • the model 1 predicts that the number of new users and sessions will reach a peak in the future 2 hours and sends the predicted value to model 2; and the model 2 analyzes that the slice expansion is required after 1.5 hours according to the predicted result from the model 1.
  • the model 2 sends the relevant capacity expansion decision to a Policy Control Function (PCF), and the PCF sends the corresponding network management policy to the NSSMF to execute the capacity expansion plan.
  • PCF Policy Control Function
  • Scenario 2 the artificial intelligence is applied to a network root cause analysis system, and accurate positioning and tracing of faults are achieved through model inference.
  • FIG. 4 is a schematic diagram based on scenario 2 according to an embodiment of the present disclosure.
  • the application instance 1 is applied to frequent alarm analysis, that is, if a certain number of specific alarms and specific events occur within a certain time, a certain correlation between the alarms and the events can be considered.
  • the application instance 2 is applied to upper and lower layered alarm analysis, namely, the influence of service faults of upper and lower layers of the same professional network is reflected in that a certain root cause alarm brings a large-area phenomenon alarm, and the root cause alarm causing the fault needs to be quickly obtained.
  • an application instance 1 (hereinafter referred to as instance 1) and an application instance 2 (hereinafter referred to as instance 2) are successfully established in a network root cause analysis system, which is informed to an orchestrator; the orchestrator activates the management entries of the artificial intelligence instances 1 and 2, and sets state machines of the instances 1 and 2 to be a running state, and sends a keep-alive message every 60 seconds to confirm the status of instances 1 and 2.
  • the data acquisition request message of instance 1 includes: IP address of instance 1 (location information of application instance) with an alarm acquisition platform used as the target network element.
  • the acquired contents include alarm information of all devices (data acquisition requirements) and the data acquisition cycle is trigger to report.
  • the data acquisition request message of instance 2 includes: the IP address of instance 2 (location information of application instance) with the alarm acquisition platform used as the target network element.
  • the acquired contents include alarm information of all network services (data acquisition requirements) and the data acquisition cycle is trigger to report.
  • the orchestrator sends the two data acquisition request messages to the alarm acquisition platform.
  • the alarm acquisition platform receives the alarm information, finds that the alarm is from a device, the content of which is "the temperature of the single board B of the network element A is too high", and sends the alarm to the application instance 1.
  • the alarm acquisition platform receives 100 same alarms as described above within 2 minutes, and then the alarm acquisition platform sends all the alarms to application instance 1.
  • the application instance 1 receives the alarm information, and the data preprocessing module filters, screens, matches, and sorts the alarm information and then sends the alarm information to a model 1.
  • the model 1 analyzes and infers such a state where the same 100 alarms are received within 2 minutes, and considers that the alarm may be caused by the temperature rise of the single board due to the inferior environment temperature and humidity.
  • Application instance 1 merges these alarms and sends an inference result to a root cause alarm collect platform.
  • the alarm acquisition platform receives the alarm information, finds that the alarm is from network, the contents of which are "link interruption of T-Multiprotocol Label Switching Section (TMS)", “link interruption of B tunnel”, “link interruption of pseudo wire C”, “service interruption of D business”, “alarm of E optical fiber Loss Signal (LOS)” and sends the alarm to the application instance 2.
  • TMS T-Multiprotocol Label Switching Section
  • LOS optical fiber Loss Signal
  • the application instance 2 receives the alarm information, and the data preprocessing module filters, screens, matches, and sorts the alarm information and then sends the alarm information to a model 2.
  • the model 2 determines that the alarms occur due to the same reason by analyzing the alarm information, namely "E optical fiber is broken and a port of the optical fiber reports an LOS alarm".
  • the application instance 2 sends the inference result to the root cause alarm collect platform.
  • Scenario 3 data screening on a preselected data feature set.
  • Table 1 is provided for understanding intelligent traffic engineering of the bearer network. Numbers in Table 1 represent feature numbering identifiers, i.e., 0-12, and texts below the numbering identifiers represent types of features.
  • the specific table is as follows: TABLE 1 0 1 2 3 Routing attributes Routing entry Virtual Private Network (VPN) information Number of users 4 5 6 7 Instantaneous user increment IP address resource utilization NAT session entry Firewall session entries 8 9 10 11 Instantaneous traffic of main path Instantaneous traffic of backup path Traffic delay of main path Traffic delay of backup path 12 Node Quality of Service (QoS) configuration
  • VPN Virtual Private Network
  • the orchestrator acquires eight data acquisition functions numbered 0, 1, 2, 3, 8, 9, 10, and 11.
  • the orchestrator therefore configures the eight data acquisition functions described above into a data feature set.
  • the target network node After receiving the data feature set, the target network node acquires the data for 0, 1, 2, 3, 8, 9, 10, and 11 via the network control device.
  • the target network node acquires the eight data acquisition functions numbered 0, 1, 2, 3, 8, 9, 10, and 11 according to the network traffic engineering characteristics of the network where the target network node is located, and performs data acquisition.
  • a device for requesting data acquisition which is configured to implement the embodiments and optional implementations as set forth above, and details of which that have been discussed will not be described again.
  • the term "module” can be a combination of software and/or hardware that implements a predetermined function.
  • the device described in the embodiments below are preferably implemented in form of software, an implementation in hardware or a combination of software and hardware is also possible and conceived.
  • Fig. 5 is a block diagram of a data processing device according to an embodiment of the present disclosure.
  • the device includes: a transmission module 52, provided in an orchestrator, and configured to generate a data acquisition request message corresponding to an application instance and send the data acquisition request message to a next communication node, wherein the next communication node includes a network control device and a target network node; an acquisition module 54, provided at the target network node, and configured to acquire data according to the data acquisition request message received from the orchestrator or via the network control device and send a data acquisition result to a processing device of the application instance; and a processing module 56, provided in the processing device of the application instance, and configured to process the data acquisition result through a model in the application instance and output a processing result.
  • a transmission module 52 provided in an orchestrator, and configured to generate a data acquisition request message corresponding to an application instance and send the data acquisition request message to a next communication node, wherein the next communication node includes a network control device and a target network node
  • the above modules can be implemented in the form of software or hardware, and the latter can be implemented by, but not limited to, the modules all arranged in the same processor or the modules arranged in different processors in any combination.
  • a storage medium having a computer program stored therein, wherein the computer program is configured to perform the steps in any one of the method embodiments as described above when executed.
  • the storage medium can be configured to store a computer program for executing steps of: S1, responsive to determining that an application instance is established, generating, by an orchestrator, a data acquisition request message corresponding to the application instance and sending the data acquisition request message to a next communication node, wherein the next communication node includes one of a network control device and a target network node; S2, acquiring, by the target network node, data according to the data acquisition request message received from the orchestrator or through the network control device, and sending a data acquisition result to a processing device of the application instance; and S3, processing, by the processing device of the application instance, the data acquisition result through a model in the application instance and outputting a processing result.
  • the storage medium can include but is not limited to various media capable of storing computer programs, such as a Universal Serial Bus (USB) disk, a Read-Only Memory (ROM), a Random-Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.
  • USB Universal Serial Bus
  • ROM Read-Only Memory
  • RAM Random-Access Memory
  • removable hard disk a hard disk
  • magnetic disk a magnetic disk
  • optical disk an optical disk.
  • an electronic device including a memory in which a computer program is stored and a processor configured to execute the computer program to perform the steps in any one of the above method embodiments.
  • the electronic device can further include a transmission device and an input/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.
  • the processor can be configured to execute, by the computer program, steps of: S1, responsive to determining that an application instance is established, generating, by an orchestrator, a data acquisition request message corresponding to the application instance and sending the data acquisition request message to a next communication node, wherein the next communication node includes one of a network control device and a target network node; S2, acquiring, by the target network node, data according to the data acquisition request message received from the orchestrator or through the network control device, and sending a data acquisition result to a processing device of the application instance; and S3, processing, by the processing device of the application instance, the data acquisition result through a model in the application instance and outputting a processing result.
  • the modules or steps of the application as described above can be implemented by a general purpose computing device. They can be centralized on a single computing device or distributed over a network of multiple computing devices, and alternatively, they can be implemented by program code executable by a computing device, such that they are stored in a memory device and executed by the computing device. In some cases, the steps as shown or described can be executed in a sequence different from that described herein, or they can be separately fabricated as individual integrated circuit modules, or multiple ones of them can be fabricated as a single integrated circuit module. Thus, the present application is not limited to any specific combination of hardware and software.

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EP20791480.5A 2019-04-15 2020-03-05 Procédé et dispositif de traitement de données, support de stockage, et dispositif électronique Pending EP3958508A4 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201910300209.2A CN111082960B9 (zh) 2019-04-15 2019-04-15 数据的处理方法及装置
PCT/CN2020/077980 WO2020211561A1 (fr) 2019-04-15 2020-03-05 Procédé et dispositif de traitement de données, support de stockage, et dispositif électronique

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Families Citing this family (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2022077198A1 (fr) * 2020-10-13 2022-04-21 Zte Corporation Configuration et sélection de réseau multiporteuse
CN115242630B (zh) * 2021-04-23 2023-10-27 中国移动通信集团四川有限公司 一种5g网络切片的编排方法、装置及电子设备
CN117597969A (zh) * 2021-09-18 2024-02-23 Oppo广东移动通信有限公司 Ai数据的传输方法、装置、设备及存储介质
CN114338421B (zh) * 2021-12-29 2024-03-26 中国电信股份有限公司 数据采集优化方法、装置、存储介质及电子设备
CN114697219B (zh) * 2022-03-24 2024-03-08 阿里巴巴（中国）有限公司 网络控制方法、直播网络的控制方法、设备及系统
CN115361308B (zh) * 2022-08-19 2024-06-25 一汽解放汽车有限公司 一种工控网络数据风险确定方法、装置、设备及存储介质
CN116567674B (zh) * 2023-07-07 2023-10-03 中国电信股份有限公司 数据处理方法、装置、网元和可读存储介质

Family Cites Families (19)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103684851B (zh) * 2013-11-26 2017-03-08 华为技术有限公司 数据采集方法和装置
BR112016026543B1 (pt) * 2014-05-15 2023-01-24 Huawei Technologies Co., Ltd Sistema de rede de virtualização de funções de rede
EP3241318B1 (fr) * 2015-01-20 2023-05-31 Huawei Technologies Co., Ltd. Procédé et appareil pour une gestion et une orchestration de virtualisation de fonction de réseau (nfv)
CN106533724B (zh) * 2015-09-11 2020-02-11 中国移动通信集团公司 监控和优化网络功能虚拟化nfv网络的方法、装置及系统
WO2017188387A1 (fr) * 2016-04-28 2017-11-02 日本電気株式会社 Dispositif, procédé et programme d'orchestration de gestion de virtualisation de fonction de réseau
WO2018128875A1 (fr) * 2017-01-05 2018-07-12 Intel IP Corporation Instanciation et gestion de fonctions de réseau physiques et virtualisées d'un nœud de réseau d'accès radio
CN108809671B (zh) * 2017-04-26 2020-10-09 华为技术有限公司 通信方法、网络设备和系统
US20180317134A1 (en) * 2017-04-28 2018-11-01 Huawei Technologies Co., Ltd. Nssmf nsmf interaction connecting virtual 5g networks and subnets
WO2018203780A1 (fr) * 2017-05-05 2018-11-08 Telefonaktiebolaget Lm Ericsson (Publ) Nœud gestionnaire et procédé réalisé dans celui-ci pour gérer une ou plusieurs fonctions de réseau dans un réseau de communication
US20180324207A1 (en) * 2017-05-05 2018-11-08 Servicenow, Inc. Network security threat intelligence sharing
CN109391504B (zh) * 2017-08-11 2022-04-29 华为技术有限公司 网络切片的部署方法和装置
US20190236485A1 (en) * 2018-01-26 2019-08-01 Cisco Technology, Inc. Orchestration system for distributed machine learning engines
US10892958B2 (en) * 2018-08-03 2021-01-12 Huawei Technologies Co., Ltd. Methods and functions of network performance monitoring and service assurance
CN112740623B (zh) * 2018-09-17 2022-08-19 华为技术有限公司 用于提供服务质量功能的设备和方法
CN109120459B (zh) * 2018-09-27 2019-09-20 中国联合网络通信有限公司广东省分公司 一种基于业务编排器的城域网业务处理方法
EP3857818B1 (fr) * 2018-09-28 2024-03-06 Nokia Technologies Oy Auto-optimisation de réseau radio basée sur des données provenant d'un réseau radio et de capteurs spatiotemporels
US11140048B2 (en) * 2019-01-11 2021-10-05 Huawei Technologies Co., Ltd. Sharable storage method and system for network data analytics
BR112021016351A2 (pt) * 2019-02-18 2021-10-26 Huawei Technologies Co., Ltd. Entidades e métodos para configuração e seleção automática de instâncias de função de rede de análise em redes 5g
US11405931B2 (en) * 2019-12-12 2022-08-02 Oracle International Corporation Methods, systems, and computer readable media for providing for network slice management using feedback mechanism

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US20220052923A1 (en) 2022-02-17
WO2020211561A1 (fr) 2020-10-22

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